Glutamate mediated dendritic and somatic plateau potentials in cortical L5 pyr cells (Gao et al '20)

 Download zip file   Auto-launch 
Help downloading and running models
Our model was built on a reconstructed Layer 5 pyramidal neuron of the rat medial prefrontal cortex, and constrained by 4 sets of experimental data: (i) voltage waveforms obtained at the site of the glutamatergic input in distal basal dendrite, including initial sodium spikelet, fast rise, plateau phase and abrupt collapse of the plateau; (ii) a family of voltage traces describing dendritic membrane responses to gradually increasing intensity of glutamatergic stimulation; (iii) voltage waveforms of backpropagating action potentials in basal dendrites (Antic, 2003); and (iv) the change of backpropagating action potential amplitude in response to drugs that block Na+ or K+ channels (Acker and Antic, 2009). Both, synaptic AMPA/NMDA and extrasynaptic NMDA inputs were placed on basal dendrites to model the induction of local regenerative potentials termed "glutamate-mediated dendritic plateau potentials". The active properties of the cell were tuned to match the voltage waveform, amplitude and duration of experimentally observed plateau potentials. The effects of input location, receptor conductance, channel properties and membrane time constant during plateau were explored. The new model predicted that during dendritic plateau potential the somatic membrane time constant is reduced. This and other model predictions were then tested in real neurons. Overall, the results support our theoretical framework that dendritic plateau potentials bring neuronal cell body into a depolarized state ("UP state"), which lasts 200 - 500 ms, or more. Plateau potentials profoundly change neuronal state -- a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to action potential firing triggered by other afferent inputs. Plateau potentials may allow cortical pyramidal neurons to tune into ongoing network activity and potentially enable synchronized firing, to form active neural ensembles.
1 . Gao PP, Graham JW, Zhou WL, Jang J, Angulo SL, Dura-Bernal S, Hines ML, Lytton W, Antic SD (2020) Local Glutamate-Mediated Dendritic Plateau Potentials Change the State of the Cortical Pyramidal Neuron. J Neurophysiol [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Prefrontal cortex (PFC); Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I A; I K; I h; I K,Ca;
Gap Junctions:
Receptor(s): Glutamate; NMDA;
Transmitter(s): Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Action Potentials; Active Dendrites; Calcium dynamics; Axonal Action Potentials; Dendritic Bistability; Detailed Neuronal Models; Membrane Properties; Synaptic Integration;
Implementer(s): Antic, Srdjan [antic at]; Gao, Peng [peng at];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; NMDA; Glutamate; I A; I K; I h; I K,Ca; Glutamate;
ampa.mod *
ca.mod *
Ca_HVA.mod *
Ca_LVAst.mod *
Cad.mod *
CaDynamics_E2.mod *
CaT.mod *
epsp.mod *
gabaa.mod *
gabab.mod *
glutamate.mod *
h_kole.mod *
h_migliore.mod *
Ih.mod *
IL.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
kadist.mod *
kaprox.mod *
kBK.mod *
kv.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
NMDA.mod *
PlateauConductance.mod *
SK_E2.mod *
SKv3_1.mod *
vecstim.mod *
vmax.mod * *

TITLE simple NMDA receptors


Essentially the same as /examples/nrniv/netcon/ampa.mod in the NEURON
distribution - i.e. Alain Destexhe's simple AMPA model - but with
different binding and unbinding rates and with a magnesium block.
Modified by Andrew Davison, The Babraham Institute, May 2000

	Simple model for glutamate AMPA receptors


    Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate
    receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used
    to estimate the parameters of the present model; the fit was performed
    using a simplex algorithm (see Destexhe et al., J. Computational Neurosci.
    1: 195-230, 1994).


    The simplified model was obtained from a detailed synaptic model that
    included the release of transmitter in adjacent terminals, its lateral
    diffusion and uptake, and its binding on postsynaptic receptors (Destexhe
    and Sejnowski, 1995).  Short pulses of transmitter with first-order
    kinetics were found to be the best fast alternative to represent the more
    detailed models.


    The first-order model can be solved analytically, leading to a very fast
    mechanism for simulating synapses, since no differential equation must be
    solved (see references below).


   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  An efficient method for
   computing synaptic conductances based on a kinetic model of receptor binding
   Neural Computation 6: 10-14, 1994.

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for
   excitable membranes, synaptic transmission and neuromodulation using a
   common kinetic formalism, Journal of Computational Neuroscience 1:
   195-230, 1994.

Orignal file by:
Kiki Sidiropoulou
Adjusted Cdur = 1 and Beta= 0.01 for better nmda spikes
PROCEDURE rate: FROM -140 TO 80 WITH 1000

Modified by Penny under the instruction of M.L.Hines on Oct 03, 2017
	Change gmax


	RANGE g, Alpha, Beta, e, gmax, ica, Cdur, iNMDA
	GLOBAL mg, Cmax
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)
	(mM) = (milli/liter)

	Cmax	= 1	 (mM)           : max transmitter concentration
	Cdur	= 1	 (ms)		: transmitter duration (rising phase)
	Alpha	= 4 (/ms /mM)	: forward (binding) rate (4)
	Beta 	=0.01   (/ms)   : backward (unbinding) rate
	e	= 0	 (mV)		: reversal potential
    mg   = 1      (mM)           : external magnesium concentration
	gmax = 1   (uS)


	v		(mV)		: postsynaptic voltage
	iNMDA 		(nA)		: current = g*(v - e)
	g 		(uS)		: conductance
	Rinf				: steady state channels open
	Rtau		(ms)		: time constant of channel binding
    B                       : magnesium block

STATE {Ron Roff}

	Rinf = Cmax*Alpha / (Cmax*Alpha + Beta)
	Rtau = 1 / (Cmax*Alpha + Beta)
	synon = 0

	SOLVE release METHOD cnexp
    B = mgblock(v)
	g = (Ron + Roff)* gmax * B
	iNMDA = g*(v - e)
    ica = 7*iNMDA/10   :(5-10 times more permeable to Ca++ than Na+ or K+, Ascher and Nowak, 1988)
    iNMDA = 3*iNMDA/10


DERIVATIVE release {
	Ron' = (synon*Rinf - Ron)/Rtau
	Roff' = -Beta*Roff

FUNCTION mgblock(v(mV)) {
        DEPEND mg
        FROM -140 TO 80 WITH 1000

        : from Jahr & Stevens

	 mgblock = 1 / (1 + exp(0.072 (/mV) * -v) * (mg / 3.57 (mM)))  :was 0.062, changed to 0.072 to get a better voltage-dependence of NMDA currents, july 2008, kiki


: following supports both saturation from single input and
: summation from multiple inputs
: if spike occurs during CDur then new off time is t + CDur
: ie. transmitter concatenates but does not summate
: Note: automatic initialization of all reference args to 0 except first

NET_RECEIVE(weight, on, nspike, r0, t0 (ms)) {
	: flag is an implicit argument of NET_RECEIVE and  normally 0
        if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse
		nspike = nspike + 1
		if (!on) {
			r0 = r0*exp(-Beta*(t - t0))
			t0 = t
			on = 1
			synon = synon + weight
			state_discontinuity(Ron, Ron + r0)
			state_discontinuity(Roff, Roff - r0)
:		 come again in Cdur with flag = current value of nspike
		net_send(Cdur, nspike)
	if (flag == nspike) { : if this associated with last spike then turn off
		r0 = weight*Rinf + (r0 - weight*Rinf)*exp(-(t - t0)/Rtau)
		t0 = t
		synon = synon - weight
		state_discontinuity(Ron, Ron - r0)
		state_discontinuity(Roff, Roff + r0)
		on = 0